TAILIEUCHUNG - Báo cáo khoa học: "Cube Summing, Approximate Inference with Non-Local Features, and Dynamic Programming without Semirings"

We introduce cube summing, a technique that permits dynamic programming algorithms for summing over structures (like the forward and inside algorithms) to be extended with non-local features that violate the classical structural independence assumptions. It is inspired by cube pruning (Chiang, 2007; Huang and Chiang, 2007) in its computation of non-local features dynamically using scored k-best lists, but also maintains additional residual quantities used in calculating approximate marginals. . | Cube Summing Approximate Inference with Non-Local Features and Dynamic Programming without Semirings Kevin Gimpel and Noah A. Smith Language Technologies Institute Carnegie Mellon University PittsbUrgh PA 15213 UsA kgimpel nasmith @ Abstract We introduce cube summing a technique that permits dynamic programming algorithms for summing over structures like the forward and inside algorithms to be extended with non-local features that violate the classical structural independence assumptions. It is inspired by cube pruning Chiang 2007 Huang and Chiang 2007 in its computation of non-local features dynamically using scored k-best lists but also maintains additional residual quantities used in calculating approximate marginals. When restricted to local features cube summing reduces to a novel semiring k-best residual that generalizes many of the semirings of Goodman 1999 . When non-local features are included cube summing does not reduce to any semiring but is compatible with generic techniques for solving dynamic programming equations. 1 Introduction Probabilistic NLP researchers frequently make independence assumptions to keep inference algorithms tractable. Doing so limits the features that are available to our models requiring features to be structurally local. Yet many problems in NLP machine translation parsing named-entity recognition and others have benefited from the addition of non-local features that break classical independence assumptions. Doing so has required algorithms for approximate inference. Recently cube pruning Chiang 2007 Huang and Chiang 2007 was proposed as a way to leverage existing dynamic programming algorithms that find optimal-scoring derivations or structures when only local features are involved. Cube pruning permits approximate decoding with non-local features but leaves open the question of how the feature weights or probabilities are learned. Meanwhile some learning algorithms like maximum likelihood for conditional log-linear

• Báo cáo khoa học
• Cube Summing
• Approximate Inference
• Non Local Features
• Dynamic Programming without Semirings
• scientific reports
• model language
• process natural language
• Dialog Control
• Natural Language System
• Dialog Control in a Natural Language System
• Expanding the Horizons
• Natural Language Interfaces
• Phil Hayes
• PROSPECTS OR PRACTICAL
• NATURAL LANGUAGE SYSTEMS
• Workshops
• NATURAL LANGUAGE
• INTERACTION WITH MACHINES
• Long Papers
• COMPUTER INTEBFACE DESIGN MURRAY TUROFF DEPARTMENT OF COMPUTER
• IiVFORMATION SCIENCE IIEW JERSEY INSTITUTE OF TECHNOLOGY
• EVALUATION OF NATURAL LANGUAGE
• INTERFACES TO DATABASE SYSTEMS
• A PANEL DISCUSSION
• database query system
• J
• Norwood Crout
• INTERFACES TO DATA BASE SYSTEMS
• Bozena Henisz
• INTERPRETING NATURAL LANGUAGE
• DATABASE UPDATES
• S
• Jermld Kaplan Jim David
• TRANSPORTABLE NATURAL LANGUAGE
• INTERFACES TO DATABASES
• Gary G
• Hendrlx and William H
• Lewis
• DATABASES THEORETICAL
• TECHNICAL ISSUES
• TRANSPORTABLE NATURAL LANGUAGE INTERFACES
• PROBLEMS AND TECHNIQUES
• Barbara J
• Grosz
• THEORETICAL TECHNICAL ISSUES
• NATURAL LANGUAGE ACCESS TO DATABASES
• R
• Petrick
• Domain Independent
• Database Access Systems
• ISSUES IN NATURAL LANGUAGE
• ACCESS TO DATABASES
• A LOGIC PROGRAMMING PERSPECTIVE
• Sharon C
• Salveter David Maier
• PLANNING NATURAL LANGUAGE
• EXPRESSIONS REFERRING
• Douglas E
• Appelt
• THE TEXT SYSTEM
• NATURAL LANGUAGE GENERATION
• Kathleen R
• M
• MENTING A DATABASE
• KNOWLEDGE REPRESENTATION
• THE KEY TO THE SELECTION PROBLEM
• E
• Jeffrey Conklin David D
• McDonald
• A KNOWLEDGE ENGINEERING
• NATURAL LANGUAGE UNDERSTANDING
• Stuart C
• Shapiro
• NATURAL LANGUAGE TEXTS
• NECESSARILY GRAMMATICAL
• EVEN COMPLETE
• UNGRAMHATICALITY
• EXTRA GRAMMATICALITY
• NATURAL LANGUAGE UNDERSTANDING SYSTEMS
• Taxonomy
• Descriptions
• Individuals in Natural
• Language Understanding
• The Use of Ooject Special Knowledge
• Natural Language Processing
• Mark H
• Bursteln